A limited number of types of sound event occur in an acoustic scene and some sound events tend to co-occur in the scene; for example, the sound events “dishes” and “glass jingling” are likely to co-occur in the acoustic scene “cooking.” In this paper, we propose a method of sound event detection using graph Laplacian regularization with sound event co-occurrence taken into account. In the proposed method, the occurrences of sound events are expressed as a graph whose nodes indicate the frequencies of event occurrence and whose edges indicate the sound event co-occurrences. This graph representation is then utilized for the model training of sound event detection, which is optimized under an objective function with a regularization term considering the graph structure of sound event occurrence and co-occurrence. Evaluation experiments using the TUT Sound Events 2016 and 2017 detasets, and the TUT Acoustic Scenes 2016 dataset show that the proposed method improves the performance of sound event detection by 7.9 percentage points compared with the conventional CNN-BiGRU-based detection method in terms of the segment-based F1 score. In particular, the experimental results indicate that the proposed method enables the detection of co-occurring sound events more accurately than the conventional method.

In this paper, a novel design method of critically sampled contourlet transform (CSCT) is proposed. The original CT which consists of Laplacian pyramid and directional filter bank provides efficient frequency plane partition for image representation. However its overcompleteness is not suitable for some applications such as image coding, its critical sampling version has been studied recently. Although several types of the CSCT have been proposed, they have problems on their realization or unnatural frequency plane partition which is different from the original CT. In contrast to the way in conventional design methods based on a "top-down" approach, the proposed method is based on a "bottom-up" one. That is, the proposed CSCT decomposes the frequency plane into small directional subbands, and then synthesizes them up to a target frequency plane partition, while the conventional ones decompose into it directly. By this way, the proposed CSCT can design an efficient frequency division which is the same as the original CT for image representation can be realized. In this paper, its effectiveness is verified by non-linear approximation simulation.

The purpose of this study is to show a class of near shift-invariant and orientation-selective transform based on even-stacked cosine-modulated filter banks (ECFBs) which originally have been proposed by Lin and Vaidyanathan. It is well-known that ECFBs can be designed by the modulation of just one prototype filter and guarantee the linear phase property. We extend this class to delay-less oversampled ECFB and show two additional attractive features; high directional selectivity and near shift-invariant property. In this paper, these properties are verified by theoretical analysis and demonstrations.

In this paper, we propose a novel lattice structure of two dimensional (2D) nonseparable linear-phase paraunitary filter banks (LPPUFBs) called 2D GenLOT. Muramatsu et al. have previously proposed a lattice structure of 2D nonseparable LPPUFBs which have efficient frequency response. However, the proposed structure requires less number of design parameters and computational costs than the conventional one. Through some design examples and simulation results, we show that both filter banks have comparable frequency response and coding gain.

In this paper, we propose a two dimensional (2D) non-separable adaptive directional lifting (ADL) structure for discrete wavelet transform (DWT) and its image coding application. Although a 2D non-separable lifting structure of 9/7 DWT has been proposed by interchanging some lifting, we generalize a polyphase representation of 2D non-separable lifting structure of DWT. Furthermore, by introducing the adaptive directional filteringingto the generalized structure, the 2D non-separable ADL structure is realized and applied into image coding. Our proposed method is simpler than the 1D ADL, and can select the different transforming direction with 1D ADL. Through the simulations, the proposed method is shown to be efficient for the lossy and lossless image coding performance.

In this paper, a linear optimization of the dual-tree complex wavelet transform (DTCWT) based on the least squares method is proposed. The proposed method can design efficient DTCWTs by improving the design degrees of freedom and solving the least square solution iteratively. Because the resulting DTCWTs have good approximation accuracy of the half sample delay condition and the stopband attenuation, they provide precise shift-invariance and directionality. Finally, the proposed DTCWTs are evaluated by applying to non-linear approximation and image denoising, and showed their effectiveness, compared with the conventional DTCWTs.

In this paper, we propose a new class of two dimensional (2D) M-channel (M-ch) non-separable filter banks (FBs) based on cosine modulated filter banks (CMFBs) via a new diagonally modulation scheme. Until now, many researchers have proposed 2D non-separable CMFBs. Nevertheless, efficient direction-selective CMFBs have not been yet. Thanks to our new modulations with diagonal shifts, proposed CMFBs have several frequency supports including direction-selective ones which cannot be realized by conventional ones. In a simulation, we show design examples of proposed CMFBs and their various directional frequency supports.